A beginner's guide to the Depth-Any-Video model by Chenxwh on Replicate

This is a simplified guide to an AI model called Depth-Any-Video maintained by Chenxwh. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.

Overview

depth-any-video estimates monocular depth maps from video input by leveraging a scalable synthetic data pipeline and generative video diffusion model priors. Created by chenxwh, this model introduces a novel approach that synthesizes 40,000 video clips from diverse game environments to train on depth estimation at scale. The architecture incorporates rotary position encoding, flow matching, and mixed-duration training to handle varying video lengths and frame rates robustly. A key innovation is the depth interpolation method that enables high-resolution depth inference with superior spatial accuracy and temporal consistency. The model processes video in overlapping windows and generates depth maps with per-frame consistency, making it suitable for applications requiring both geometric accuracy and smooth temporal coherence across sequences.

Best use cases

3D scene reconstruction and structure-from-motion pipelines: The model's strong temporal consistency and high-resolution depth capability make it valuable for reconstructing 3D environments from monocular video. Video game footage, architectural walkthroughs, or camera pan footage can be converted into depth sequences that inform 3D mesh generation or SLAM systems. The 40,000 synthetic training clips from games ensure the model generalizes well to structured indoor and outdoor environments common in reconstruction tasks.

Video-based visual effects and depth-aware compositing: Filmmakers and VFX artists can extract per-frame depth information to create depth-of-field effects, parallax depth-based layering, or 3D-aware compositing without specialized depth sensors. The temporal consistency prevents flickering artifacts that plague frame-by-frame depth estimation, enabling smooth depth-based effects across entire scenes.

Robotic perception and autonomous navigation prototyping: Mobile robots and autonomous systems benefit from monocular depth estimation when stereo or LiDAR sensors are unavailable. The model's ability to handle variable frame rates and video lengths accommodates different camera systems and real-time constraints in robotics applications.

Depth-guided video enhancement and super-resolution: Depth maps serve as auxiliary information for video enhancement tasks like deblurring, motion blur removal, or view synthesis. The temporal coherence ensures depth-guided operations produce consistent results across frames without per-frame artifacts.

3D video generation and novel view synthesis: Applications requiring depth as intermediate representations for generating novel viewpoints or 3D video formats can leverage the model's dense depth sequences to establish geometric correspondence between frames.

Limitations

The model requires video input or individual images and produces depth map outputs only—it does not generate novel content or modify visual appearance. Maximum resolution defaults to 1024 pixels and can be increased to 2048, but higher resolutions scale computational cost significantly. The num_frames parameter must be an even number and defaults to 32 frames per inference chunk, meaning long videos require sequential processing with overlapping windows (controlled by num_overlap_frames). Processing speed depends on video duration, frame count, and resolution; the README shows inference examples but does not specify latency or hardware requirements.

The model handles variable frame rates but was trained primarily on game footage with synthetic data, so performance on highly dynamic real-world scenarios, extreme lighting conditions, reflections, or transparent/thin objects may degrade. Depth estimation fails on featureless regions, uniform surfaces, and high-motion blur. The depth output is relative and monocular, not metric depth—it represents ordinal depth relationships rather than absolute distances. The Replicate version produces a single output URI (the depth map file), limiting real-time streaming or batch multi-format output. The default of 3 denoise steps keeps inference fast but may reduce quality; step count ranges from 1-3 with no validation for higher values. No explicit license restrictions are documented beyond the GitHub repository license, but commercial use rights should be verified against the original LICENSE file.

How it compares

depth-anything-v2 prioritizes single-image depth estimation with faster inference, fewer parameters, and higher absolute depth accuracy on benchmarks. Choose depth-any-video when temporal consistency across video frames is critical; choose depth-anything-v2 when processing isolated images or when inference speed is the primary constraint.

depth-anything-video by fal-ai produces per-frame depth estimation with temporal consistency and offers multiple model sizes (Small, Base, Large) plus 5 colormaps, side-by-side visualization, and raw .npz depth export. It provides more output format flexibility and explicit model size options. Use depth-any-video for maximum depth quality and temporal coherence from synthetic training; use depth-anything-video when you need export flexibility or want to trade quality for inference speed by selecting a smaller model variant.

DepthCrafter generates consistent long depth sequences optimized for open-world videos with a focus on handling very long sequences. Both models address temporal consistency, but DepthCrafter specializes in extended-duration video while depth-any-video balances quality and frame rate handling through its mixed-duration training strategy.

depth-anything is the foundational single-image depth model trained on 1.5M labeled and 62M+ unlabeled images, providing robust general-purpose monocular depth. Choose depth-any-video when video temporal coherence matters; choose depth-anything for simpler image-by-image processing or when training data diversity (rather than synthetic depth data) is valued.

Technical specifications

The model is built on video diffusion model architecture with flow matching and rotary position encoding to handle variable-length video sequences. Training leveraged 40,000 synthetic video clips from diverse games, enabling strong generalization to structured environments. The depth interpolation method enhances spatial resolution without retraining the core depth estimator.

Key architectural parameters and defaults:

• Frame processing: Processes videos in chunks of num_frames (default 32, must be even) with overlapping windows controlled by num_overlap_frames (default 6) and decode_chunk_size (default 16 frames per decode forward pass)
• Interpolation: num_interp_frames (default 16) controls the number of frames used for depth inpainting refinement
• Inference quality: denoise_steps (default 3, range 1-3) controls denoising iterations; fewer steps trade quality for speed
• Resolution handling: max_resolution defaults to 1024 pixels, supports up to 2048; higher values increase memory and computation
• Input flexibility: Accepts both images and videos; controlled by input_is_video boolean (default true)
• Reproducibility: Optional seed parameter for deterministic outputs
• Output format: Single depth map file (typically .npz or similar depth encoding)

The model runs on Replicate's GPU infrastructure with no explicit CPU-only option. Training dataset comprised 40,000 game-sourced video clips with synthetic depth ground truth, enabling robust depth estimation without labeled real-world video data.

Model inputs and outputs

Inputs

• input_image_or_video (string, URI): Path to input image or video file; required
• input_is_video (boolean, default: true): Specify whether input is video (true) or image (false)
• denoise_steps (integer, default: 3): Number of denoising steps; range 1-3
• num_frames (integer, default: 32): Number of frames per inference chunk; must be even
• decode_chunk_size (integer, default: 16): Frames to decode per forward pass
• num_interp_frames (integer, default: 16): Frames used for inpaint-based depth refinement
• num_overlap_frames (integer, default: 6): Overlap between sequential inference windows
• max_resolution (integer, default: 1024): Maximum resolution for depth inference; supports up to 2048
• seed (integer, optional): Random seed for reproducibility; leave blank to randomize

Outputs

• Output (string, URI): Single depth map file containing estimated depth for input video or image; format depends on Replicate processing pipeline (commonly .npz or depth-encoded binary)

Getting started

import replicate

client = replicate.Replicate()

output = client.run(
    "chenxwh/depth-any-video:4055afe2aba32ab3d2dbccc4fabb240bf643db7fe56f33c1f9535d948b219495",
    input={
        "input_image_or_video": "https://example.com/video.mp4",
        "input_is_video": True,
        "denoise_steps": 3,
        "num_frames": 32,
        "decode_chunk_size": 16,
        "num_interp_frames": 16,
        "num_overlap_frames": 6,
        "max_resolution": 1024,
        "seed": 42
    }
)

print(output)

Frequently asked questions

Q: Does this model produce metric depth or ordinal depth?

A: The model produces ordinal (relative) depth maps that capture geometric relationships between scene elements, not absolute metric distances. Use it for scene understanding, relative depth ordering, and effects that depend on depth hierarchy rather than real-world distance measurements.

Q: Can I process long videos, and how does chunking work?

A: Yes. Videos longer than num_frames (default 32) are automatically processed in overlapping chunks using num_overlap_frames (default 6) to maintain temporal consistency across segment boundaries. You control chunk size and overlap via parameters to balance memory usage and temporal coherence.

Q: What happens if my video has variable frame rates?

A: The model handles variable frame rates through its mixed-duration training strategy and flow matching architecture, though optimal results occur when input is standardized to a consistent frame rate. For inconsistent sources, pre-process video to a target frame rate before submission.

Q: How does denoise_steps affect output quality and speed?

A: Fewer denoising steps (1-2) reduce inference time but may produce noisier or less detailed depth maps. The default of 3 steps provides a balance; use fewer steps for speed-critical applications and the maximum (3) for quality-critical tasks.

Q: Is this model suitable for production applications?

A: The model is stable and deployed on Replicate, making it suitable for production use cases where temporal depth consistency matters. However, test it on your specific video content type—performance is optimized for game footage and structured environments, and may degrade on highly dynamic real-world footage with extreme lighting or transparent objects.

Q: What resolution should I use for my workflow?

A: Default to 1024 for balanced speed and quality. Increase to 2048 if spatial detail is critical and latency is acceptable. Higher resolutions scale memory and computation quadratically; test incrementally to find the optimal point for your hardware.

Q: What output format does the depth map use?

A: The model outputs depth as a single URI file (typically binary or .npz encoding) containing per-frame depth maps. Export formats and visualization options depend on Replicate's post-processing; export as raw depth values for downstream processing in 3D reconstruction or robotics pipelines.

Q: How does this compare to frame-by-frame depth estimation?

A: This model enforces temporal consistency across frames using video diffusion priors and overlapping window inference, eliminating the flickering and jitter common in applying single-frame depth models independently to each video frame. The trade-off is longer processing time for higher temporal coherence.

Click here to read the full guide to Depth-Any-Video